store = {}
store['args']={'batch_size': 64, 'scoring_batch_size': 512, 'test_batch_size': 512, 'validation_set_size': 1024, 'early_stopping_patience': 3, 'epochs': 30, 'epoch_samples': 5056, 'num_inference_samples': 20, 'available_sample_k': 40, 'num_iterations': 20, 'no_cuda': False, 'name': 'bald_40_332152', 'seed': 332152, 'log_interval': 20, 'type': 'AcquisitionFunction.bald'}
store['iterations']=[]
store['initial_samples']=[5266, 6421, 535, 47388, 19737, 21174, 28276, 4755, 21797, 5331, 39966, 36366, 48362, 31516, 31996, 2844, 10358, 8951, 30334, 17142]
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.618, 'nll': 2.2874437576293944}, 'chosen_samples': [59823, 10909, 14093, 5364, 45609, 39293, 6270, 20471, 5137, 43020, 19539, 24223, 6882, 43083, 4488, 48934, 632, 20647, 8683, 30097, 40169, 4990, 23919, 8116, 20007, 19685, 48604, 55244, 30350, 56401, 58469, 13893, 43013, 54725, 22034, 21608, 17542, 47690, 18504, 43961], 'chosen_samples_score': ['1.0471413', '1.0520694', '1.0505086', '1.0521445', '1.060509', '1.0654017', '1.0574887', '1.0529485', '1.0604037', '1.0658557', '1.0803134', '1.0792603', '1.0755987', '1.0667357', '1.0714407', '1.077647', '1.0664661', '1.0821356', '1.085749', '1.0933063', '1.0960779', '1.0996158', '1.0931817', '1.0987186', '1.0893521', '1.0900685', '1.1018391', '1.1110582', '1.1394608', '1.1641587', '1.2646046', '1.1075273', '1.1155423', '1.1544411', '1.119493', '1.1063323', '1.1737688', '1.1658618', '1.1233639', '1.1387976']})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.6764, 'nll': 1.5943767135620117}, 'chosen_samples': [49181, 45488, 54413, 22105, 45003, 19042, 13666, 38109, 991, 244, 20093, 53079, 23696, 21375, 44591, 37825, 28029, 32022, 10918, 59465, 56770, 36244, 1568, 35662, 16320, 17127, 23997, 1409, 29530, 23069, 30954, 54191, 222, 13127, 25321, 20169, 35331, 36984, 50025, 52862], 'chosen_samples_score': ['0.8753321', '0.875754', '0.8786171', '0.8792795', '0.8909301', '0.92327684', '0.88922644', '0.8914797', '0.88711256', '0.9463469', '0.900558', '0.89537144', '0.8864352', '0.95591396', '0.9311109', '1.0139573', '0.8885225', '0.97331065', '0.897338', '0.89845204', '0.8991637', '0.91454524', '0.9024252', '0.92532694', '0.90341514', '0.9067813', '0.9107582', '0.8890313', '0.93149', '0.8930412', '0.8842385', '0.9258166', '0.91782', '0.9095963', '0.93124074', '0.92515194', '0.8926222', '0.89817375', '0.90529186', '0.9276533']})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.8128, 'nll': 0.9647393035888672}, 'chosen_samples': [15870, 5968, 3772, 52624, 55402, 4481, 30646, 12839, 570, 25495, 3524, 51738, 31430, 19981, 35232, 32678, 27680, 8886, 28471, 4218, 45516, 42948, 161, 38760, 45255, 8961, 50736, 45972, 7796, 2352, 24558, 56664, 5393, 57910, 14749, 32140, 11202, 47383, 30925, 33228], 'chosen_samples_score': ['0.7846352', '0.7865549', '0.78663695', '0.7930398', '0.79013115', '0.7883807', '0.7935672', '0.79216313', '0.79654676', '0.79828745', '0.79034144', '0.78897434', '0.79207116', '0.79882973', '0.8360219', '0.81094223', '0.8231627', '0.8275432', '0.8332141', '0.8109312', '0.8283243', '0.81552726', '0.83399844', '0.8066945', '0.80312186', '0.8060909', '0.823704', '0.84209394', '0.82647216', '0.8104988', '0.79999', '0.8164513', '0.84567696', '0.87159985', '0.9329005', '0.8509', '0.8500563', '0.8469983', '0.8646449', '0.9918087']})
store['iterations'].append({'num_epochs': 5, 'test_metrics': {'accuracy': 0.7901, 'nll': 0.9609455041885376}, 'chosen_samples': [28229, 19904, 5129, 40976, 42477, 3972, 53854, 59128, 48507, 30824, 42763, 21880, 20158, 34758, 47365, 2966, 16049, 48323, 8045, 44416, 32065, 14765, 36744, 25281, 59638, 38916, 44219, 27641, 34672, 29589, 20557, 58275, 40905, 7979, 31545, 29713, 28455, 10255, 27878, 45753], 'chosen_samples_score': ['0.8673494', '0.86874825', '0.86891687', '0.87286764', '0.86944574', '0.87343425', '0.87393266', '0.8761206', '0.8749422', '0.88047916', '0.8797987', '0.8820417', '0.8774715', '0.88101006', '0.88100654', '0.8824441', '0.9214725', '0.8841092', '0.9324879', '0.9397149', '0.8861504', '0.9004086', '0.8892322', '0.8856861', '0.9393874', '0.8927882', '0.898292', '0.9147056', '0.89818466', '0.9205963', '0.9817308', '1.0562367', '0.9456882', '0.893192', '0.9658108', '0.8950255', '0.9134584', '0.95352614', '0.93993664', '1.0443518']})
store['iterations'].append({'num_epochs': 6, 'test_metrics': {'accuracy': 0.8933, 'nll': 0.6533001677513123}, 'chosen_samples': [59314, 49666, 55639, 35628, 5679, 41445, 46640, 15551, 8893, 29659, 19362, 3252, 26132, 20641, 6650, 49536, 40702, 11586, 12066, 37407, 384, 31975, 31593, 49472, 26208, 49543, 28512, 52133, 27540, 7418, 51230, 16692, 31396, 57311, 12493, 27248, 11931, 32774, 37584, 27696], 'chosen_samples_score': ['0.9264796', '0.926593', '0.9272809', '0.9302985', '0.9300378', '0.9304138', '0.93069535', '0.9291105', '0.9299882', '0.9314011', '0.9479278', '0.9883874', '0.9773811', '0.96189845', '0.9438092', '0.95443857', '0.9570269', '0.9408087', '0.9569011', '0.96820277', '0.9449548', '0.95105034', '0.9705212', '0.9516566', '0.9506817', '0.97758055', '0.9385121', '0.97068965', '0.9576024', '0.95245945', '0.95703', '0.957408', '0.98877174', '1.0392222', '0.995683', '1.0425482', '1.0206506', '1.0248973', '1.0895333', '1.015165']})
store['iterations'].append({'num_epochs': 9, 'test_metrics': {'accuracy': 0.9178, 'nll': 0.5636810350418091}, 'chosen_samples': [8517, 37900, 46132, 38974, 30962, 40654, 51819, 15855, 14690, 32280, 36415, 56346, 52034, 13156, 43532, 9503, 26162, 10012, 54994, 25661, 4111, 26756, 21333, 14581, 55992, 55743, 19163, 10481, 10210, 14735, 17756, 36072, 49957, 59726, 53324, 45777, 32355, 7264, 46406, 18473], 'chosen_samples_score': ['0.95313686', '0.96546626', '0.9535319', '0.9684587', '0.96504736', '0.96933466', '0.9654744', '0.9703137', '0.973219', '0.9866425', '0.98466897', '0.97479004', '0.9818242', '0.97947055', '0.9751096', '0.98599297', '0.9746956', '0.9719922', '0.9824678', '0.98683184', '0.9866664', '0.99257517', '1.0045636', '1.0110478', '0.99394536', '1.0070283', '0.9928045', '1.0298259', '1.0620034', '1.0165222', '1.0435598', '1.0915985', '1.0696023', '1.1228024', '1.0019248', '1.1322796', '1.0620295', '0.9993939', '1.0127716', '0.9966183']})
store['iterations'].append({'num_epochs': 6, 'test_metrics': {'accuracy': 0.9389, 'nll': 0.45564099855422974}, 'chosen_samples': [17634, 47320, 20985, 29153, 3765, 47513, 4328, 44865, 31650, 31664, 34188, 19495, 2934, 40434, 44810, 47587, 27928, 7322, 9449, 22497, 15191, 37489, 41924, 9147, 41453, 59681, 39359, 17507, 38549, 41722, 16756, 30142, 32427, 46412, 52099, 12497, 52169, 55095, 2765, 6755], 'chosen_samples_score': ['0.7995895', '0.7999808', '0.8000288', '0.8062578', '0.8045897', '0.8145339', '0.80030257', '0.8203266', '0.826967', '0.80154544', '0.8188948', '0.8171824', '0.81059617', '0.8086062', '0.82703125', '0.8313187', '0.8335348', '0.82970345', '0.83626074', '0.85881865', '0.8433249', '0.9620403', '0.8481197', '0.8579927', '0.987545', '0.88985866', '0.9081259', '1.0081861', '0.84335035', '0.862242', '0.86848664', '0.8577149', '0.9597062', '0.95278835', '0.8741215', '0.92122895', '0.9817677', '0.87425894', '0.8439989', '0.85151255']})
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store['iterations'].append({'num_epochs': 8, 'test_metrics': {'accuracy': 0.9562, 'nll': 0.3803784944534302}, 'chosen_samples': [36417, 12404, 27456, 6050, 14654, 25159, 1682, 20859, 26882, 46088, 16836, 5295, 16997, 11657, 49515, 31941, 18042, 16196, 36268, 49002, 31637, 39208, 44969, 54950, 2426, 38389, 33982, 12650, 42774, 17728, 8464, 17209, 52937, 8268, 5175, 2728, 45739, 23723, 54885, 21601], 'chosen_samples_score': ['0.84629893', '0.84678096', '0.85460246', '0.84930205', '0.85490644', '0.9205044', '0.8754896', '0.870923', '0.8952767', '0.89966303', '0.8598901', '0.90421784', '0.8783727', '0.8717396', '0.90431523', '0.9324874', '0.9094909', '0.8566608', '0.86588776', '0.8605114', '1.0434592', '0.87950885', '0.935041', '0.85604006', '0.9500333', '0.88293153', '0.8567518', '0.87210363', '0.92297655', '0.8931327', '0.8878211', '0.8639636', '0.89105016', '0.90442854', '0.98522997', '0.8835585', '0.94115263', '0.86447024', '0.87874824', '0.8735123']})
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